Customer service

On Blits.ai this is an AI agent grounded in a knowledge base with hybrid retrieval over the organization's approved content, with flows for journeys that must follow fixed steps and custom functions for read only lookups such as request or case status. Language detection and multi language content let one agent serve customers in their own language.

The same agent runs on voice (streaming speech recognition and synthesis, DTMF, call transfer), web chat, WhatsApp, SMS and Facebook Messenger, and inside the organization's own app through the API channel. Human handover passes the conversation transcript to a live agent platform (Salesforce, Freshdesk or Zoho SalesIQ) or transfers the call on voice, and supervisors can monitor and take over live, including on calls.

Guardrails check input and output, PII masking runs at the gateway, test suites hold multi turn conversation sets built from real transcripts that you run before each change goes live, monitors check key journeys on a schedule, and analytics show interactions, satisfaction, sentiment and top intents, with conversation logs to review every handover. The platform is model agnostic.

Run Blits.ai in our cloud in the EU, Qatar, Singapore, the UAE, the US or Australia, in your private cloud, or fully on premises.

On Blits.ai the building blocks are voice telephony with real time streaming speech recognition across several providers (with self hosted transcription and speaker diarization available where recordings must stay on Blits.ai infrastructure), an AI agent grounded in a knowledge base of approved procedures with hybrid retrieval, and summarization through an agent with structured output that maps to CRM fields. The assistant is exposed to the agent desktop through the REST or WebSocket API, and custom functions write the confirmed summary to the CRM.

PII masking at the gateway keeps sensitive data away from the model and logs, and card numbers typed in chat are detected and tokenized there; for payment calls on voice, plan redaction or pause and resume as a design step. Test suites grade summaries and suggestions against reviewed examples on every change, and the platform is model agnostic. Run Blits.ai in our cloud in the EU, Qatar, Singapore, the UAE, the US or Australia, in your private cloud, or fully on premises.

On Blits.ai this is an AI agent over a knowledge base with hybrid retrieval (dense vectors plus BM25), ingesting PDFs, Office documents, email files and crawled web pages, and a central document library with version control and revert, so owners can replace superseded documents. SQL knowledge bases add structured data where answers need numbers. SharePoint and OneDrive are available as ready made tools, and Confluence is in the integration catalog.

The same agent serves Microsoft Teams, the intranet through the web widget, and other assistants through the REST API, so one retrieval layer backs several front doors. Guardrails block prompt injection and unsafe output, PII masking protects personal data, and role based access control limits who manages which agents and content. Test suites with LLM grading that uses knowledge base evidence run the evaluation set on every change, and analytics with response feedback show untrained questions and unexpected answers for content owners. The platform is model agnostic. Run Blits.ai in our cloud in the EU, Qatar, Singapore, the UAE, the US or Australia, in your private cloud, or fully on premises.

On Blits.ai this is an AI agent with custom functions that call the retailer's order management, carrier tracking and returns APIs (REST calls with their own authentication and limits), and a knowledge base with the approved delivery, returns and warranty content, retrieved with hybrid search. Return eligibility and refund rules run as deterministic steps in a flow or inside the function, so the model explains the decision rather than making it; larger refunds can go through an agentic workflow with human in the loop approval above a set threshold.

The same agent serves web chat, WhatsApp, email and voice, and a mobile app through the REST or WebSocket API channel; the chat widget can show orders and receipts as rich cards. Guardrails check input and output, PII masking and card number tokenization happen at the gateway, and human handover escalates the conversation to the service team, including through Salesforce, Freshdesk or Zoho SalesIQ. Test suites run multi turn order and return conversations as regression tests after each change, and monitors check the order lookup against a test order on a schedule. The platform is model agnostic, so the model can be chosen or switched per agent.

On Blits.ai the scoring runs as an agentic workflow: interactions from the platform's own voice and chat channels, or recordings fetched through custom functions, are transcribed with streaming speech recognition or the self hosted transcription option with speaker diarization, then an AI agent with structured output scores each one against the rubric held in a knowledge base, citing the excerpt behind each finding.

Findings above a threshold go to a reviewer through human in the loop approval before anything is recorded, and every run keeps an audit trail. Chat traffic on the platform's own channels passes the gateway, where PII masking and card number tokenization apply before it reaches the chat backend. Recordings fetched into the workflow do not pass the gateway, so add a redaction step in the workflow before scoring.

Test suites with LLM grading hold the human calibration set and are rerun after every rubric or model change, so agreement with reviewers is tracked over time. The platform is model agnostic. Run Blits.ai in our cloud in the EU, Qatar, Singapore, the UAE, the US or Australia, in your private cloud, or fully on premises.

On Blits.ai this runs as an agentic workflow on a schedule: custom functions pull new feedback from survey, review and ticketing systems through their REST APIs (the integration catalog includes Zendesk, Salesforce and ServiceNow) and strip names, account numbers and contact details before the data reaches the agent, and an AI agent with structured output tags each comment with themes from your codebook, sentiment per theme and any suggested action. The results are written to a SQL database registered as a SQL knowledge base, so an agent can answer plain language questions from analysts and theme owners over them.

For feedback that comes in through Blits.ai channels, PII masking at the gateway removes personal data from chat input, and the built in analytics already show satisfaction and sentiment (with engines from Amazon, Google, IBM and Microsoft), thumbs up and down feedback with comments on responses, and conversation level CSAT ratings.

Test suites with deterministic rules for label matching (or LLM based grading for summaries) let you run your labeled comments through the classifier after every prompt change, human in the loop approval holds routing of sensitive comments until a person confirms, and monitors run scheduled checks against the classifier agent and alert when it fails. The platform is model agnostic.

Run Blits.ai in our cloud in the EU, Qatar, Singapore, the UAE, the US or Australia, in your private cloud, or fully on premises.